ffinn ships a production-hardened core. The AI agent adds your custom features on top - following every pattern, enforcing every rule, generating every layer - from database to UI.
Before the AI agent writes a single line, ffinn already ships with everything that every application needs - and that nobody wants to write twice. These modules are protected files the AI agent can never modify.
That protection is intentional. Authentication, billing, and permissions are the most critical parts of any application. They stay hand-written, reviewed, and version-controlled by you.
Layered Architecture
Each step is designed to keep you in control while the AI handles the heavy lifting.
Navigate to AI Agent → Settings and add your API credentials. ffinn supports OpenAI, Anthropic, Google Gemini, Azure OpenAI, and Ollama for fully local generation.
Supported Providers
Link your GitHub, GitLab, or Azure DevOps repository. The AI agent reads your codebase to understand existing patterns before it writes a single line.
Configured Per Generation
Describe the feature you want in plain language. The more context you provide, the better the output. You don't need to know the codebase structure - the agent figures that out.
Example Prompts
Before generating anything, the agent fetches existing files from the bounded context - currently-existing models, services, and endpoints that neighbouring code touches. Output is architecturally consistent from the start.
Files Typically Generated
Every generated file is run through a validator before it is committed. If the code violates architecture rules, the agent self-corrects - up to 3 times - with a focused prompt explaining each violation.
Self-Correction Loop
Generated files are committed to a new branch in your repository. Open the pull request to see a clean diff - exactly what changed versus main. Approve, request changes, or close it - you stay in control.
Branch Naming
When you're happy with the review, trigger a staging deployment directly from the AI Agent dashboard. The agent merges the branch, runs your CI workflow, and reports the deployment URL back in real time via WebSocket.
Staging Checklist
Exercise your new feature against real data in staging. If something needs fine-tuning, write a follow-up prompt - the AI agent generates an incremental patch on top of the existing generation.
Iterating on Generations
One click in the dashboard triggers your production workflow. The same CI/CD pipeline - same migrations, same artifact. Your new feature is live.
Deployment Audit Trail
You don't re-generate from scratch when requirements change. Each follow-up prompt reads the existing generated code as context and applies a surgical, reviewable patch.
Prompt
"Add a nullable notes field to the Customer entity and surface it in the form"
Alembic migration + model update + schema update + form field added
Prompt
"Restrict Customer delete to admin role only, and log the action to the audit trail"
Router permission decorator updated + audit log call added in service
Prompt
"Add an export-to-CSV endpoint for Customers filtered by date range"
New router endpoint + query filter + streaming CSV response
Prompt
"When a new Customer is created, send them a welcome email using the email template system"
Email service call injected into Customer service create method
Every file the agent writes is validated against a strict set of architectural rules before it is committed. Violations trigger a targeted self-correction loop, not a fatal error.
We're putting the finishing touches on the launch. Sign up for early access and be the first to build with ffinn.
Bring your own API keys. No markup. No vendor lock-in.